Dynamic compressive mechanical responses of selective laser-melted Ti-6Al-4V alloy were studied in terms of the influences of scanning speed, building angle as well as impacting strain rate. It was found that the ultimate flow stress and energy absorption increased first and then dropped sharply as the scanning speed increased from 1.0 to 1.6 m/s, showing that the sample built at scanning speed of 1.2 m/s possessed the best dynamic mechanical properties. They increased straightly up as the building angle increased from 0° to 90°, but only the sample built at 45° ruptured with shearing fracture pattern. Moreover, the samples exhibited distinct strain rate hardening effect, as the applied strain rate increased from 900 to 2100/s, and the sample ruptured ultimately with mixed ductility/brittle fracture pattern. Volume fraction of LAGBs in samples increased from 9.1 to 18.9% and 21.4% after impacting at strain rates of 900/s and 2100/s, indicating that intenser dislocation was activated at a higher strain rate impacting, this is the main cause of enhancement in strength. This study provided an insight into the influence of high strain rate impact loading on the dynamic mechanical responses of SLMed TC4 alloy, which is conducive to further exploiting the performance potential of the SLMed materials.

Influence of process parameter and strain rate on the dynamic compressive properties of selective laser-melted Ti-6Al-4V alloy

Shock test is a pivotal stage for designing and manufacturing space instruments. As the essential components in shock test systems to measure shock signals accurately, high-g accelerometers are usually exposed to hazardous shock environment and could be subjected to various damages. Owing to that these damages to the accelerometers could result in erroneous measurements which would further lead to shock test failures, accurately diagnosing the fault type of each high-g accelerometer can be vital to ensure the reliability of the shock test experiments. Additionally, in practice, an accelerometer in one malfunction form usually outputs mutable signal waveforms, so that it is difficult to empirically judge the fault type of the accelerometer based on the erroneous readings. Moreover, traditional hardware diagnosis approaches require disassembling the sensor’s package shell and manually observing the damage of the elements inner the sensor, which are less-efficient and uneconomical. Aiming at these problems, several data-driven approaches are incorporated to diagnose the fault types of high-g accelerometers in this work. Firstly, several high-g accelerometers with most frequent types of damage are collected, and a shock signal dataset is gathered by conducting shock tests on these faulty accelerometers. Then, the obtained dataset is used to train several base classifiers to identify the fault types in a supervised fashion. Lastly, a hybrid ensemble learning model is established by integrating these base classifiers with both heterogeneous and homogeneous models. Experimental results show that these data-driven methods can accurately identify the fault types of high-g accelerometers from their mutable erroneous readings.

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On Fault Diagnosis for High-G Accelerometers via Data-Driven Models

In the aerospace industry, pyroshock testing is an indispensable step in designing space electronics. Yet, damages in high-g accelerometers, the core measuring instruments in pyroshock test systems, could result in various failures of pyroshock tests. To ensure the reliability of pyroschock tests for space electronics, a machine learning system is proposed to perform self-validation for high-g accelerometers. In this work, self-validation refers to the capability of identifying five key parameters, namely the validated shock signal, the validated uncertainty, the measurement status, the raw shock signal and the fault type, synchronously during measuring shock signals in pyroshock tests. To achieve the highest performance, we accomplish these tasks through combining an ensemble learning model and a deep neural network (DNN). The ensemble learning model, which integrates several k-nearest neighbors with different k values, is used to identify the sensors’ health conditions from their measurements and diagnose their fault types synchronously if damaged. The DNN, a deep autoencoder-based neural network, is designed to correct corrupted measurements through constructing the mapping between faulty signals and their corresponding reference counterparts. Experimental results show that the proposed machine learning system is capable of not only accurately identifying the health conditions and fault types of the damaged high-g accelerometers from their measurements, but also recovering the corrupted shock signals to a large extent, and, meanwhile, outputting the five self-validation parameters.

Self-validating high-g accelerometers through data-driven methods

Effect of heating treatment on the microstructural evolution and dynamic tensile properties of Ti-6Al-4V alloy produced by selective laser melting

A deep learning based approach is proposed to accurately recover shock signals measured from a damaged high-g accelerometer without modifying the hardware. We first conducted shock tests and collected a large dataset of shock signals with different levels of acceleration by using an efficient experimental apparatus. The training data is composed of a pair of signals simultaneously obtained from a faulty accelerometer and a high-end accelerometer (served as the ground truth). A customized autoencoder neural network is designed and trained on this dataset, aiming to map the faulty signals to their reference counterparts. Experimental results show that, with the help of deep learning, shock signals can be accurately recovered from the faulty measurements. Compared with conventional approaches that require diagnosing and replacing faulty parts, the proposed data-driven method demonstrates a highly promising solution that allows recovering corrupted signals without introducing extra work to upgrade the hardware at almost zero cost. The dataset and code of this work are made publicly available on GitHub at https://github.com/hope-yao/Sensor_Calibration .

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A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

A nonlinear dynamic model and parameters identification method for predicting the shock pulse of rubber waveform generator

The invention relates to an impact response spectrum generator applied to a vertical impact tester. The impact response spectrum generator comprises an excitation cushion block, a frame assembly and aresonant response plate. The excitation cushion block is provided with a rubber pad at one end, has an end not provided with a rubber pad and fixedly connected to the falling tabletop of the verticalimpact tester, and is located at the center of the falling tabletop. The axis of the excitation cushion block is perpendicular to the falling tabletop. The resonant response plate is located in the frame assembly and is connected with the frame assembly through an elastic connecting member. When the frame assembly falls down with the falling tabletop, the end having the rubber pad of the excitation cushion block passes through the bottom of the frame assembly, collides with the resonant response plate, generates an explosion impact signal, and obtains an impact response spectrum. The impact response spectrum generator is simple in structure and convenient to disassemble, realizes double functions of the impact tester, reduces cost and improves simulation reliability.

Impact response spectrum generator applied to vertical impact tester

The invention relates to a blade inherent frequency automatic testing system which comprises an external control analysis system, a base, a stepping motor, a rotating disc, a clamping assembly, a laser displacement sensor and a jetting assembly. The stepping motor drives the rotating disc to axially rotate so as to drive the to-be-tested blade to rotate; the jetting end interruptedly injects the gas at equal time to enable the to-be-tested blade to vibrate, and meanwhile, the vibration displacement of the to-be-tested blade is measured through the laser displacement sensor, and the obtained vibration displacement information and the vibration frequency information are transmitted to the external control analysis system, so that the inherent frequency of each order of the to-be-tested bladeis obtained. The middle test process does not need manual intervention, the automation degree is high, the requirement for the technical level of an operator is low, and the testing efficiency is high. The technical level and the testing efficiency of related industries can be greatly improved, and the application prospect is good.

Blade inherent frequency automatic testing system

Thermal contact resistance(TCR) is one of the important parameters in heat transfer problems of engineering, and it is necessary to estimate the value of TCR effectively in many engineering fields. Considering the limitation of current estimation methods of TCR such as only focusing on one-dimensional thermal conduction, getting a single value of TCR merely, and the temperature measuring points only being placed in temperature gradient direction of mediums, boundary element method(BEM) and conjugate gradient method are combined to estimate the TCR in planar mediums. The value of TCR in relation to the position of contact interface line is estimated with this method, and the positions of temperature measuring points can be selected randomly because of the characteristic of BEM that there is no necessity to discrete the inner area and it is sufficient to discrete the boundary. The analysis of calculation examples base on heat transfer model of planar medium demonstrates that:this method can estimate the TCR effectively, but the ill-posedness is also existed in this method which is one of the inverse problems, and the calculation error of TCR is increased with the distance from temperature measuring points to contact interface, the estimation precision and stability can be improved after optimization with least square method.

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Study on Thermal Contact Resistance Estimation in Planar Medium

Firstly, we get the completely bouncing criteria Cr for droplet on moving substrate. The bouncing without splashing condition is $\mathrm{Cr}>1$. Then, we mainly research the effect of wind field for droplet, and get the completely bouncing criteria $\mathrm{Cr}_{\mathrm{wind}}$ for droplet with wind. Lastly, we get the contact angle of droplet on the moving substrate and calculate the Time Independent Reynolds Equation with $rho$ and $\mu$ are constant.

Chengwu Liu

Papers

A nonlinear dynamic model and parameters identification method for predicting the shock pulse of rubber waveform generator

Impact response spectrum generator applied to vertical impact tester

Blade inherent frequency automatic testing system

Study on Thermal Contact Resistance Estimation in Planar Medium

Bounced Model of Droplet on Moving Substrate